Applications of Multivariate Data Analysis in Biotech Processing

Multivariate data analysis (MVDA) is quickly gaining popularity both in basic research and applied scientific fields as a statistical method of choice for examining variable interactions that were previously undefined. Multivariate data analysis by means of projection and regression methods overcomes challenges associated with applications such as multidimensionality of the data set, missing data, and variation introduced by disturbing factors such as experimental error and noise. This article presents how four of the major biotech companies, Amgen, Genentech, Wyeth Biotech, and MedImmune, are using multivariate analysis to solve problems encountered in biotech processing.

Anurag S. Rathore

As the biotech industry moves toward implementing the initiative of Quality by Design, using statistical tools for design of experiments and for data analysis is becoming necessary. It has been pointed out that biopharmaceutical manufacturing data is complex and univariate or bivariate analysis can often be inefficient and result in misleading conclusions.1,2 Principal component analysis (PCA), partial least squares (PLS), and multiple regression are some of the commonly used projection and regression methods in MVDA. Additionally, multivariate statistical process control (SPC) charts are useful in routine monitoring of manufacturing processes.

Recently, several studies have addressed the topic of performing multivariate analysis on data from fermentation and cell culture operations.3–5 This article is the tenth in the "Elements of Biopharmaceutical Production" series and presents how four of the major biotech companies, Amgen, Genentech, Wyeth Biotech, and MedImmune, are using multivariate analysis to solve problems encountered in biotech processing.

USE OF MVDA TO OPTIMIZE LARGE-SCALE PRODUCTION CULTURE PERFORMANCE

Rob Johnson and Oliver Yu, Genentech, Inc.

The multiple linear regression of historical data from a licensed antibody process revealed opportunities for improved production culture performance and more consistent product quality by optimizing process parameters in their acceptable ranges stated in the product filing. This application demonstrates the utility of MVDA for capitalizing on these opportunities for a significant increase in production culture yield and a sustained decrease in process variability.

Figure 1. Control chart of an antibody process shows production cultures performing at 70% of the previous campaign

At the start of the third manufacturing campaign of a commercial antibody process, production culture productivity was 70% of the previous campaign average, as shown in Figure 1. This observed decrease in culture productivity and increase in process variability led to an effort to understand and reduce the sources of variability in the process.

The multivariate approach used here was simple multiple linear regression (MLR) achieved in an iterative backwards stepwise fashion against data from the previous campaign. The assumption of linearity is often acceptable since nonlinear relationships are well-approximated by a line when constrained in a small range, as is the case for commercial production. The iterative backwards stepwise approach minimizes expert bias because the computer iterates toward maximum correlation and minimum model error.